ML4GW
Torch utilities for training neural networks in gravitational wave physics applications.
Documentation
Please visit our documentation page to see descriptions and examples of the functions and modules available in ml4gw.
We also have an interactive Jupyter notebook demonstrating much of the core functionality available here.
To run this notebook, download it from the above link and follow the instructions within it to install the required packages.
See also the documentation page for the tutorial to look
through it without running the code.
Installation
Pip installation
You can install ml4gw with pip:
pip install ml4gw
To build with a specific version of PyTorch/CUDA, please see the PyTorch installation instructions here to see how to specify the desired torch version and --extra-index-url flag. For example, to install with torch 2.5.1 and CUDA 11.8 support, you would run
pip install ml4gw torch==2.5.1--extra-index-url=https://download.pytorch.org/whl/cu118
uv installation
If you want to develop ml4gw, you can use uv to install the project in editable mode.
For example, after cloning the repository, create a virtualenv using
uv venv --python=3.11
Then sync the dependencies from the uv lock file using
uv sync --all-extras
Code changes can be tested using
uv run pytest
See contribution guide for more details.
Contributing
If you come across errors in the code, have difficulties using this software, or simply find that the current version doesn't cover your use case, please file an issue on our GitHub page, and we'll be happy to offer support. If you want to add feature, please refer to the contribution guide for more details. We also strongly encourage ML users in the GW physics space to try their hand at working on these issues and joining on as collaborators! For more information about how to get involved, feel free to reach out to any of the maintainers listed in the pyproject.toml. By bringing in new users with new use cases, we hope to develop this library into a truly general-purpose tool that makes deep learning more accessible for gravitational wave physicists everywhere.
Funding
We are grateful for the support of the U.S. National Science Foundation (NSF) Harnessing the Data Revolution (HDR) Institute for Accelerating AI Algorithms for Data Driven Discovery (A3D3) under Cooperative Agreement No. PHY-2117997.
1 maintainer
Release files for ml4gw 0.8.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ml4gw-0.8.4.tar.gz | 131.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ml4gw-0.8.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 275.0 kB
Release files / ml4gw-0.8.4.tar.gz
| Download URL | ml4gw-0.8.4.tar.gz |
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| Size | 131.4 kB |
| Tags | Source |
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Release files / ml4gw-0.8.4-py3-none-any.whl
| Download URL | ml4gw-0.8.4-py3-none-any.whl |
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| Size | 143.6 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
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